Dongjiang Ji

Adaptive window and nonlinear gradient recursive least squares for few-view low-dose CT denoising

ZhenSen Li, Dongjiang Ji, ShiDi Yang, Ran Li et autres

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Coupling shearlet coefficients and spatial information from a single image to construct a tensor for sparse-view tomography reconstruction

Qi Zhang, Dongjiang Ji, Yuxuan Zhou, Lili Zhang et autres

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Variable Step Normalized Least Mean Square Guided by Composite Desired Signal for Few-View Computed Tomography Denoising

Yuxuan Zhou, Dongjiang Ji, Qi Zhang

Background: Low-dose CT provides essential diagnostic information while minimizing radiation exposure through few-view reconstruction techniques. However, these techniques often introduce noise and artifacts, affecting diagnostic accuracy. Although L 0 -smoothing regularization methods partially address these issues, their fixed sparsity constraint cannot adapt …

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Score-based generative model framework with physical prior constraints for propagation-based X-ray phase-contrast CT reconstruction from incomplete projections

Fangzhi Li, Chenyang Ma, Ying–Jun Angela Zhang, Yimin Li et autres

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Impact of the Non-Negativity Constraint for In-Line Phase Contrast Computed Tomography

Lisha Wu, Jing Huang, Dongjiang Ji

In-line X-ray phase-contrast computed tomography (IL-PCCT) can reconstruct high resolution images, and the core of IL-PCCT is the image reconstruction algorithm. As we all know, image reconstruction algorithm can suppress artifacts and produce high-quality reconstructed images using a priori constraint information. For …

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Data-driven and model-guided iterative reconstruction framework for simultaneous sparse-view and ring artifact reduction in synchrotron X-ray microtomography

Fangzhi Li, Yimin Li, Ziyao Wang, Chenyang Ma et autres

Synchrotron X-ray microtomography (S-µCT) is a highly valuable technique for investigating organ function and pathologies. However, its application is often limited by high radiation doses and the occurrence of ring artifacts. While S-µCT utilizing sparse-view projections can effectively decrease radiation doses, the …

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A Multiscale Reconstruction Framework Based on Edge-Enhanced Guidance for In-Line X-Ray Phase-Contrast CT With Limited-Angle Projections

Yimin Li, Yuanyuan Zhao, Chenyang Ma, Fangzhi Li et autres

The in-line X-ray phase-contrast computed tomography (IL-XPCCT) serves as an effective tool for studying organ function and pathologies. However, IL-XPCCT approaches often result in high radiation doses due to long scan times. To address this issue, a limited angel sampling strategy is …

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Adaptive orthogonal directional total variation with kernel regression for CT image denoising

Xiying Xue, Dongjiang Ji, Chunyu Xu, Yuqing Zhao et autres

BACKGROUND: Low-dose computed tomography (CT) has been successful in reducing radiation exposure for patients. However, the use of reconstructions from sparse angle sampling in low-dose CT often leads to severe streak artifacts in the reconstructed images. OBJECTIVE: In order to address this …

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A Hybrid Wavelet and TV Regularization Model for Single Image Dehazing Based on Dark Channel Prior

CHUNYU XU, Dongjiang Ji, Xiying Xue

In hazy conditions, the image obtained by the image sensor may have low visibility, poor contrast, and other degradation phenomena due to the scattering of suspended particles in the atmosphere. To address the limitations of the dark channel prior algorithm, which may …

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Image Dehazing with Hybrid λ2-λ0 Penalty Mode

Yuxuan Zhou, Dongjiang Ji, Chunyu Xu

Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this work proposes a new hybrid λ2-λ0 penalty model …

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Fused Analytical and Iterative Reconstruction Based on Detail Transfer For Few-View Computed Tomography

ZhenSen Li, Yimin Li, Dongjiang Ji, Yuqing Zhao et autres

Truncated total variation in fractional B-spline wavelet transform for micro-CT image denoising

Dongjiang Ji, Xiying Xue, Chunyu Xu

BACKGROUND: In medical applications, computed tomography (CT) is widely used to evaluate various sample characteristics. However, image quality of CT reconstruction can be degraded due to artifacts. OBJECTIVE: To propose and test a truncated total variation (truncation TV) model to solve the …

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